In this paper, we propose a novel multi-scale attention based network (called MSA-Net) for feature matching problems. Current deep networks based feature matching methods suffer from limited effectiveness and robustness when applied to different scenarios, due to random distributions of outliers and insufficient information learning. To address this issue, we propose a multi-scale attention block to enhance the robustness to outliers, for improving the representational ability of the feature map. In addition, we also design a novel context channel refine block and a context spatial refine block to mine the information context with less parameters along channel and spatial dimensions, respectively. The proposed MSA-Net is able to effectively infer the probability of correspondences being inliers with less parameters. Extensive experiments on outlier removal and relative pose estimation have shown the performance improvements of our network over current state-of-the-art methods with less parameters on both outdoor and indoor datasets. Notably, our proposed network achieves an 11.7% improvement at error threshold 5° without RANSAC than the state-of-the-art method on relative pose estimation task when trained on YFCC100M dataset.

We develop a conceptually simple, flexible, and effective framework (named T-Net) for two-view correspondence learning. Given a set of putative correspondences, we reject outliers and regress the relative pose encoded by the essential matrix, by an end-to-end framework, which is consisted of two novel structures: "−" structure and "|" structure. " − " structure adopts an iterative strategy to learn correspondence features. "|" structure integrates all the features of the iterations and outputs the correspondence weight. In addition, we introduce Permutation-Equivariant Context Squeeze-and-Excitation module, an adapted version of SE module, to process sparse correspondences in a permutation-equivariant way and capture both global and channel-wise contextual information. Extensive experiments on outdoor and indoor scenes show that the proposed T-Net achieves state-of-the-art performance. On outdoor scenes (YFCC100M dataset), T-Net achieves an mAP of 52.28%, a 34.22% precision increase from the best-published result (38.95%). On indoor scenes (SUN3D dataset), T-Net (19.71%) obtains a 21.82% precision increase from the best-published result (16.18%). Source code: https://github.com/x-gb/T-Net.

Establishing reliable correspondences from a putative correspondence set is a challenging task. Most of state-of-the-art methods utilize the local context and global context to address the task. However, the local and global context often contains large number of outliers, which have a negative impact on capturing scene geometry. In this paper, we propose a Multi-scale Attention Network (called MANet), which introduces the attention mechanism for feature matching, to improve the ability of capturing scene geometry. Specifically, we first fuse the features of low and high levels by an multi-scale strategy network to enhance the representative ability of features. Then, we propose an attentive PointCN block and an attentive pooling layer, to discriminatively capture global context and local context information, respectively. We demonstrate through extensive experiments on both indoor and outdoor datasets that MANet provides a significant improvement in the performance of the two-view geometry and correspondences accuracy compared to the state-of-the-art methods.